Modélisation et analyse du fonctionnement d’un système de stockage intégré au réseau électrique

RÉSUMÉ : Ce projet de recherche traite de la problématique suivante : comment déterminer la valeur ajoutée d’un système de stockage au réseau électrique, du point de vue de la gestion des ressources et de l’amélioration de l’efficacité énergétique ? Le cas considéré dans l’analyse est celui d’un système de stockage de batteries intégré au réseau électrique québécois. Dans le contexte de cette étude, les rôles du stockage sont les suivants : réserve de secours en cas d’urgence, lissage de la charge et exportation pour les périodes de pointe. L’objectif principal de ce travail de recherche est d’étudier le dimensionnement de ce système de stockage et comment un tel système de stockage affecte la stratégie de production et d’exportation d’électricité tout en maximisant le bénéfice apporté par ce système. Afin d’atteindre ces objectifs, deux modèles d’optimisation sont développés. De plus, nous étudions les impacts des principaux paramètres utilisés (demande prévisionnelle, coût d’investissement, prix d’exportation, etc.) pour ce calcul sur les résultats. Le critère d’optimisation a conduit à privilégier l’utilisation de la programmation linéaire en nombres entiers. Ces modèles sont basés sur certaines des caractéristiques typiques des modèles d’optimisation du fonctionnement de la batterie et celles de la centrale électrique. Les variables de décision incluent le nombre de chaque type de batteries, la quantité d’électricité chargée et déchargée par les batteries à chaque instant, la quantité d’énergie totale dans les batteries à chaque instant et la quantité d’électricité produite et exportée en temps réel. La solution est limitée principalement par les contraintes de fonctionnement de la production (capacité installée, coût unitaire d’électricité), les caractéristiques techniques et économiques des batteries, la demande interne et externe. Les modèles ont été codés en AMPL et la résolution de ce programme a été réalisée avec le solveur CPLEX. Suite à cette résolution, les résultats ont permis de constater que la valeur liée au système de stockage dépend de la capacité de la production installée, du coût unitaire d’électricité, de la demande interne et externe, du prix d’électricité, du prix d’exportation et de la politique d’exportation. Bien que les observations effectuées aient permis de calculer les bénéfices d’un système de stockage de façon détaillée et d’en tirer des conclusions importantes concernant les paramètres d’entrée, leurs impacts et leurs interactions, une évaluation à la fois technique et économique plus détaillée sur un tel projet d’investissement s’avère nécessaire.----------ABSTRACT : This research addresses the following problem: How to determine the added value of a storage system to the grid, in terms of resource management and improvement of energy efficiency? The case considered in this analysis is a battery storage system integrated into the Quebec power grid. In this study, the functions of storage are: reserves back-up in case of emergency, load leveling to reduce the fluctuation of power generation and stocking the production excess to export. The main objective of this research is to study the capacity design of the storage system and how such storage system could affect the strategy of electricity production and exportation, while maximizing the profit brought by the storage system. To achieve these objectives, two optimization models were developed. In addition, we investigate the effect of key parameters used (demand forecasting, cost of investment, export prices, etc.) on the results. The optimization method tends to favor the use of mixed integer linear programming (MILP). Both models are based on some of the typical characteristics of optimization models for batteries and those of the power plant. The decision variables include the number of each type of batteries, the level of electricity charged and discharged by the batteries at a given time set the amount of electricity produced and exported, etc. The solution is largely limited by the constraints of production (maximum capacity, cost of production), the technical and economic characteristics of the batteries, internal and external demand. The models were coded in AMPL and solving this program was achieved with the CPLEX solver. Following this resolution, the results have shown that the values related to the storage system depends on the installed production capacity, the cost of electricity, the internal and external demand, the electricity prices, the export policy, etc. Although observations have allowed to calculate the benefits of a storage system in a detailed way and to draw important conclusions about the input parameters, their impacts and their interactions, a further study both technical and economic on such project to evaluate the investment decision is definitely needed

COMPUTATIONAL COMMUNICATION INTELLIGENCE: EXPLORING LINGUISTIC MANIFESTATION AND SOCIAL DYNAMICS IN ONLINE COMMUNICATION

We now live in an age of online communication. As social media becomes an integral part of our life, online communication becomes an essential life skill. In this dissertation, we aim to understand how people effectively communicate online. We research components of success in online communication and present scientific methods to study the skill of effective communication. This research advances the state of art in machine learning and communication studies. For communication studies, we pioneer the study of a communication phenomenon we call Communication Intelligence in online interactions. We create a theory about communication intelligence that measures participants’ ten high-order communication skills, including restraint, self-reflection, perspective taking, and balance. We present a multi-perspective analysis for understanding communication intelligence, including its diverse language, shared linguistic characteristics across people, social dynamics, and the effects of communication modality on communication intelligence. For machine learning, we contribute new computational models and formulations for addressing multi-label and multi-task machine learning problems. We develop a new hierarchical probabilistic model for simultaneously identifying multiple intelligence-embodied communication skills from natural language. The model learns the topic assignment for each sentence and provides a practical and simple way to determine document labels without relying on a threshold function. The model performance increases as the number of labels grows, which makes it a promising approach for large-scale data analysis. We also develop a new multi-task formulation for simultaneously identifying multiple intelligence-embodied communication skills from lexical, discourse, and interaction features. The key merit of this model is that it is a general multi-task formulation that unifies many widely used regularization techniques, including Lasso, group Lasso, sparse-group Lasso, and the Dirty model. This model expands the applicability of multi-task learning by allowing analyzing real-world problems where the degree of task relatedness is uncertain and the true structure of the groups in data is not clear ahead of time. Moreover, it can be applied to streaming data to perform large-scale analysis in real time. Beyond the application of studying communication intelligence, the developed models and formulations can also benefit research in other areas where the problems of simultaneously predicting multiple categories are abundant

Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularization

To enhance the cross-target and cross-scene generalization of target-driven visual navigation based on deep reinforcement learning (RL), we introduce an information-theoretic regularization term into the RL objective. The regularization maximizes the mutual information between navigation actions and visual observation transforms of an agent, thus promoting more informed navigation decisions. This way, the agent models the action-observation dynamics by learning a variational generative model. Based on the model, the agent generates (imagines) the next observation from its current observation and navigation target. This way, the agent learns to understand the causality between navigation actions and the changes in its observations, which allows the agent to predict the next action for navigation by comparing the current and the imagined next observations. Cross-target and cross-scene evaluations on the AI2-THOR framework show that our method attains at least a $10\%$ improvement of average success rate over some state-of-the-art models. We further evaluate our model in two real-world settings: navigation in unseen indoor scenes from a discrete Active Vision Dataset (AVD) and continuous real-world environments with a TurtleBot.We demonstrate that our navigation model is able to successfully achieve navigation tasks in these scenarios. Videos and models can be found in the supplementary material.Comment: 11 pages, corresponding author: Kai Xu ([email protected]) and Jun Wang ([email protected]

Starch/microcrystalline cellulose hybrid gels as gastric-floating drug delivery systems

We report hybrid gels based on a high-amylose starch and microcrystalline cellulose with demonstrated properties for gastric-floating drug delivery purposes. The starch/cellulose gels were prepared by ionic liquid dissolution and regeneration, resulting in a continuous surface and a porous interior and a type-II crystalline structure of cellulose. These polysaccharide gels displayed satisfactory elasticity (0.88), recovery (0.26–0.36) and equilibrium swelling (1013–1369%). The hybrid gels were loaded with ranitidine hydrochloride as a model drug and subsequently, low-density starch/cellulose tablets were fabricated by vacuum-freeze-drying. In vitro tests in a simulated gastric fluid indicate that the 3:7 (wt./wt.) starch/cellulose system could maintain the buoyancy for up to 24 h with a release of 45.87% for the first 1 h and a sustained release for up to 10 h. Therefore, our results have demonstrated the excellent gastric-floating ability and sustainable drug release behavior of the starch/cellulose hybrid gels

Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services

Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, traditional model serving paradigms usually resort to the cloud by fully uploading geo-distributed input data to remote datacenters. However, our empirical measurements reveal the significant communication overhead of such cloud-based serving and highlight the profound potential in applying the emerging fog computing. To maximize the architectural benefits brought by fog computing, in this paper, we present Fograph, a novel distributed real-time GNN inference framework that leverages diverse and dynamic resources of multiple fog nodes in proximity to IoT data sources. By introducing heterogeneity-aware execution planning and GNN-specific compression techniques, Fograph tailors its design to well accommodate the unique characteristics of GNN serving in fog environments. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and fog deployment by up to 5.39x execution speedup and 6.84x throughput improvement.Comment: Accepted by IEEE/ACM Transactions on Networkin

Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning

We present a target-driven navigation system to improve mapless visual navigation in indoor scenes. Our method takes a multi-view observation of a robot and a target as inputs at each time step to provide a sequence of actions that move the robot to the target without relying on odometry or GPS at runtime. The system is learned by optimizing a combinational objective encompassing three key designs. First, we propose that an agent conceives the next observation before making an action decision. This is achieved by learning a variational generative module from expert demonstrations. We then propose predicting static collision in advance, as an auxiliary task to improve safety during navigation. Moreover, to alleviate the training data imbalance problem of termination action prediction, we also introduce a target checking module to differentiate from augmenting navigation policy with a termination action. The three proposed designs all contribute to the improved training data efficiency, static collision avoidance, and navigation generalization performance, resulting in a novel target-driven mapless navigation system. Through experiments on a TurtleBot, we provide evidence that our model can be integrated into a robotic system and navigate in the real world. Videos and models can be found in the supplementary material.Comment: 11 pages, accepted by IEEE Robotics and Automation Letter